En la aplicación con datos se busca encontrar la relación entre la precipitación diaria (en mm) y el caudal diario del rÃo (en \(m^3/s\)) de dos rÃos donde un rÃo desemboca en el otro en una región del departamento del Cauca en Colombia.La precipitación se midió en la estación meteorológica San Juan con una altitud de 2400 metros. El primer caudal del rÃo se midió en la estación hidrológica ‘’El Trébol’’ del rÃo Bedón con una altitud de 1720 metros y el segundo caudal se midió en el rÃo de la Plata en la estación hidrológica de ‘’Villalosada’’ con una altitud de 1300 metros. Las estaciones están ubicadas cerca del ecuador de la Tierra en una zona geográfica muy seca. Esta última caracterÃstica permite el control de factores hidrológicos/meteorológicos, los cuales pueden distorsionar el tipo de relación dinámica explicada por el modelo MTAR.
El perÃodo de tiempo que se considero es del 1 de enero de 2006 al 14 de abril de 2009 (1200 puntos de tiempo) que tiene 57 puntos de tiempo con datos faltantes en la serie de precipitaciones, 214 en la serie del caudal del rÃo Bedon y 213 en la serie del cauce del rÃo de la Plata. Estos datos fueron proporcionados por el IDEAM (disponibles en la libreria R para los modelos TAR y MTAR).
# [Lectura de Datos]
data("hydrodata")
Yt = hydrodata[,3:4]
Pt = hydrodata$Rainfall
data_miss = list(Yt = as.matrix(log(sqrt(Yt[-1,]))),
Zt = as.matrix(sqrt(Pt[-nrow(hydrodata)])))
## Graficas de las series ..................................................####
datos = tsregime(Yt = data_miss$Yt,Zt = data_miss$Zt)Yt, Zt and Xt admit NA values use mtarmissing for estimation autoplot(datos,1) + autoplot(datos,2)
Para la aplicación de la metodologÃa para estimación de modelos MTAR con datos faltantes se recurre a la Liberia de R ‘’MTAR’’ (librerÃa basada en el mismo artÃculo y que implementa toda la metodologÃa de estimación). Como primer paso y como se propone en el articulo se consideran las series transformadas \(Y_t = (\ln{\sqrt(Y_{1t}}),\ln(\sqrt{Y_{2t}}))\) y \(Z_t = \sqrt{P_{t-1}}\) (precipitaciones y el caudal bivariado de los rÃos Bedón y La Plata el dÃa t). En la metodologÃa de estimación para estos modelos, el primer paso corresponde a la imputación inicial de datos faltantes que en este caso se realiza por medio de un promedio móvil sobre cada uno de los componentes.
Y_temp = datos$Yt
Y_temp[,1] = imput_y(Y_temp[,1])
Y_temp[,2] = imput_y(Y_temp[,2])
Z_temp = datos$Zt
Z_temp[,1] = imput_y(Z_temp[,1])
datos_temp = tsregime(Yt = Y_temp,Zt = Z_temp)
autoplot(datos_temp,1) + autoplot(datos_temp,2)Una vez completada la serie, el siguiente paso corresponde a la estimación del número de regÃmenes mediante el método de selección de modelos de ‘’Carlin and Chib’’ y el uso del criterio ‘’NAIC’‘, seguido de la estimación de ordenes para el modelo por medio de la metodologÃa de’‘Kuo and Mallick’’ (ambas metodologÃas explicadas en la primera parte de estos informes). Para ello, se usan las funciones ‘’mtarnumreg()’’ y ‘’mtarstr()’’ que implementan dichas metodologÃas usando prioris no informativas (el usuario podrÃa dar prioris informativas si es el caso).
# initial = mtarinipars(tsregime_obj = datos_temp,list_model = list(l0_max = 3),method = 'KUO')
# estim_nr = mtarnumreg(ini_obj = initial,iterprev = 2000,niter_m = 3000,burn_m = 1000,
# list_m = TRUE,ordersprev = list(maxpj = 5,maxqj = 0,maxdj = 5))
# autoplot(estim_nr,1) + autoplot(estim_nr,2)Después de encontrar un aproximación a los ordenes de los rezagos (parámetros estructurales) asà como del valor del umbral es posible una primera estimación de los datos faltantes, esto a partir de la función ``mtarmissing()’’ observando en la gráfica el valor puntual y su respectivo intervalo de credibilidad al 95% en azul para los valores faltantes.
# list_model = list(pars = list(l = 2,r = 2.6,orders = list(pj = c(5,5),dj = c(4,5))))
# initial = mtarinipars(tsregime_obj = tsregime(Yt = data_miss$Yt,Zt = data_miss$Zt),list_model = list_model)
# missingest = mtarmissing(ini_obj = initial,chain = TRUE, niter = 5000,burn = 1000)
# data_c = missingest$tsregim
autoplot(missingest,4)[[1]] + autoplot(missingest,4)[[2]]
Una vez completados los valores faltantes es necesario realizar nuevamente el procedimiento tanto de estimación de la cantidad de régimenes como de los parámetros estructurales para seguir reestimando los valores faltantes hasta tener una convergencia. Una vez concluido este proceso y teniendo los valores finales tanto de los faltantes como de los parámetros estructurales se realiza la estimación final de los parámetros no estructurales por medio de la función ``mtarns()’’ para los cuales evaluamos el comportamiento de las cadenas y supuestos sobre los residuos al ajuste final.
Los primeros gráficos nos muestran un buen comportamiento de cadenas sin valores que salten, por su parte los residuos aunque presenten algunas inconsistencias en normalidad podemos ver que no son autocorrelacionados, sin embargo es posible considerar otros ajustes al modelo para garantizar los supuestos.
# initial = mtarinipars(tsregime_obj = data_c,list_model = list(pars = list(l = 2,orders = list(pj = c(5,5),dj = c(4,5)))))
# ns2 = mtarns(ini_obj = initial,niter = 15000,chain = TRUE,burn = 5000,r_init = 2.6)
autoplot(ns2,1)
autoplot(ns2,2)$R1+autoplot(ns2,2)$R2
autoplot(ns2,3)$R1$c+autoplot(ns2,3)$R2$cautoplot(ns2,3)$R1$phi+autoplot(ns2,3)$R2$phi
autoplot(ns2,3)$R1$delta+autoplot(ns2,3)$R2$deltadiagnostic_mtar(ns2)[[2]] + diagnostic_mtar(ns2)[[3]]
diagnostic_mtar(ns2)[[4]] + diagnostic_mtar(ns2)[[5]]
diagnostic_mtar(ns2)[[6]]
Finalmente vemos la estimación de los parámetros con valor de umbral estimado 2.54 y en el gráfico el ajuste del modelo a la serie de datos una vez completados los valores faltantes.
ns2$regime
autoplot(ns2,5)Con lo anterior ya es posible realizar el pronóstico de la serie, por medio de la función ``mtarforecast()’’ considerando \(h = 10\) observamos el prónostico 10 pasos adelante tanto de la variable de respuesta como umbral con su respectivo intervalo de credibilidad al 95%
pred1 = mtarforecast(ns2,h = 10)Estimating model Ut = (Zt,Xt)
Estimating non-structural parameters with threshold(s) known ...Reg_1
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|===========================================================================| 100% elapsed=01m 32s, remaining~00sSaving results ...
| | 0 % elapsed=00s
|= | 0 % elapsed=00s, remaining~02m 30s
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|= | 0 % elapsed=01s, remaining~02m 60s
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|= | 1 % elapsed=01s, remaining~02m 60s
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|= | 1 % elapsed=01s, remaining~03m 01s
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|= | 1 % elapsed=01s, remaining~03m 01s
|= | 1 % elapsed=01s, remaining~02m 60s
|= | 1 % elapsed=01s, remaining~03m 01s
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|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
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|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 57s
|= | 1 % elapsed=02s, remaining~02m 58s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 59s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~02m 60s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~03m 00s
|= | 1 % elapsed=02s, remaining~03m 01s
|= | 1 % elapsed=02s, remaining~03m 01s
|== | 1 % elapsed=02s, remaining~02m 60s
|== | 1 % elapsed=02s, remaining~03m 00s
|== | 1 % elapsed=02s, remaining~03m 01s
|== | 1 % elapsed=02s, remaining~03m 01s
|== | 1 % elapsed=02s, remaining~03m 01s
|== | 1 % elapsed=02s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~03m 01s
|== | 1 % elapsed=03s, remaining~02m 60s
|== | 1 % elapsed=03s, remaining~02m 60s
|== | 1 % elapsed=03s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~03m 01s
|== | 1 % elapsed=03s, remaining~03m 01s
|== | 1 % elapsed=03s, remaining~02m 60s
|== | 1 % elapsed=03s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~03m 01s
|== | 1 % elapsed=03s, remaining~02m 60s
|== | 1 % elapsed=03s, remaining~02m 60s
|== | 1 % elapsed=03s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~02m 59s
|== | 1 % elapsed=03s, remaining~02m 59s
|== | 1 % elapsed=03s, remaining~02m 60s
|== | 1 % elapsed=03s, remaining~03m 00s
|== | 1 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 58s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 59s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~02m 60s
|== | 2 % elapsed=03s, remaining~03m 01s
|== | 2 % elapsed=03s, remaining~03m 01s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 04s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 03s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
|== | 2 % elapsed=04s, remaining~03m 02s
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|===========================================================================| 100% elapsed=04m 56s, remaining~00spred1$forecast$estim$Yt
lower limit 2.5% mean upper limit 97.5% FNDP
1200.1 2.82644647 3.45470323 4.0693048 1.898453
1200.2 -0.10040909 0.17532251 0.4502183 1.898453
1201.1 1.35819270 2.03207597 2.7033500 1.860717
1201.2 -0.25189363 0.17031936 0.6064289 1.860717
1202.1 1.14496819 1.72169348 2.2929976 1.897720
1202.2 -0.21804761 0.24986784 0.7197960 1.897720
1203.1 3.97159859 4.40987018 4.8618904 1.910525
1203.2 -0.11280745 0.17109252 0.4596773 1.910525
1204.1 2.49106817 3.18607876 3.8794081 1.926146
1204.2 -0.48999243 -0.04297338 0.4056911 1.926146
1205.1 0.78056975 1.47831827 2.1803403 1.907639
1205.2 -0.40687854 0.14861441 0.7071511 1.907639
1206.1 4.47424293 5.02676298 5.5744084 1.871195
1206.2 -0.08191974 0.25991463 0.6057236 1.871195
1207.1 3.41141532 4.34747826 5.2951206 1.906519
1207.2 -0.61752360 -0.10922778 0.4009244 1.906519
1208.1 2.23257810 3.38603575 4.5337105 1.916673
1208.2 -2.06822489 -0.81768260 0.4357648 1.916673
1209.1 4.52211013 5.21629594 5.9292391 1.919916
1209.2 -0.17478916 0.33066470 0.8450098 1.919916
$Zt
lower limit 2.5% mean upper limit 97.5% FNDP
1200.1 -0.6466044 2.004539 4.706301 1.898453
1201.1 -0.6777297 1.955306 4.621715 1.860717
1202.1 -1.0290016 1.672624 4.368263 1.897720
1203.1 -1.1337334 1.623128 4.332484 1.910525
1204.1 -0.3358724 2.349589 5.056558 1.926146
1205.1 -0.4069809 2.309134 5.048625 1.907639
1206.1 -1.0726941 1.626381 4.290429 1.871195
1207.1 -0.7708655 1.968999 4.703153 1.906519
1208.1 -0.5446227 2.143045 4.830108 1.916673
1209.1 0.1783136 2.905230 5.601415 1.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